The following best practices provide guidance on controlling query computation.
For an example of examining CPU time, see the query plan explanation of time on CPU-bound tasks.
Avoid repeatedly transforming data via SQL queries
Best practice: If you are using SQL to perform ETL operations, avoid situations where you are repeatedly transforming the same data.
For example, if you are using SQL to trim strings or extract data by using regular expressions, it is more performant to materialize the transformed results in a destination table. Functions like regular expressions require additional computation. Querying the destination table without the added transformation overhead is much more efficient.
Use approximate aggregation functions
Best practice: If your use case supports it, use an approximate aggregation function.
If the SQL aggregation function you're using has an equivalent approximation
function, the approximation function will yield faster query performance. For
example, instead of using
more information, see approximate aggregation functions
in the standard SQL reference.
You can also use HyperLogLog++ functions to do approximations (including custom approximate aggregations). For more information, see HyperLogLog functions in the standard SQL reference.
Order query operations to maximize performance
Best practice: Use
ORDER BY only in the outermost query or within window
clauses (analytic functions). Push complex operations to the end of the query.
If you need to sort data, filter first to reduce the number of values that you
need to sort. If you sort your data first, you sort much more data than is
necessary. It is preferable to sort on a subset of data than to sort all the
data and apply a
When you use an
ORDER BY clause, it should appear only in the outermost query.
ORDER BY clause in the middle of a query greatly impacts performance
unless it is being used in a window (analytic) function.
Another technique for ordering your query is to push complex operations, such as regular expressions and mathematical functions to the end of the query. Again, this technique allows the data to be pruned as much as possible before the complex operations are performed.
Optimize your join patterns
Best practice: For queries that join data from multiple tables, optimize your join patterns. Start with the largest table.
When you create a query by using a
JOIN, consider the order in which you are
merging the data. The standard SQL query optimizer can determine which table
should be on which side of the join, but it is still recommended to order your
joined tables appropriately. The best practice is to place the largest table
first, followed by the smallest, and then by decreasing size.
When you have a large table as the left side of the
JOIN and a small one on
the right side of the
JOIN, a broadcast join is created. A broadcast join
sends all the data in the smaller table to each slot that processes the larger
table. It is advisable to perform the broadcast join first.
To view the size of the tables in your
JOIN, see getting information about tables.
Prune partitioned queries
Best practice: When querying a time-partitioned table,
_PARTITIONTIME pseudo column to filter the partitions.
When you query partitioned tables, use the
_PARTITIONTIME pseudo column.
Filtering the data using
_PARTITIONTIME allows you to specify a date or range
of dates. For example, the following
WHERE clause uses the
pseudo column to specify partitions between January 1, 2016 and January 31, 2016:
WHERE _PARTITIONTIME BETWEEN TIMESTAMP(“20160101”) AND TIMESTAMP(“20160131”)
The query processes data only in the partitions that are indicated by the date range. Filtering your partitions improves query performance and reduces costs.